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Papers by Suryansh Upadhyay

arXiv (Cornell University), May 2, 2023

Security and reliability are primary concerns in any computing paradigm, including quantum comput... more Security and reliability are primary concerns in any computing paradigm, including quantum computing. Currently, users can access quantum computers through a cloud-based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect that more companies including untrusted/less-trusted/unreliable vendors 1 will begin offering quantum computers as hardware-as-a-service at varied price/performance points. Since computing time on quantum hardware is expensive and the access queue could be long, the users will be motivated to use the cheaper and readily available but unreliable/less-trusted hardware. The lesstrusted vendors can tamper with the results, providing a suboptimal solution to the user. For applications such as, critical infrastructure optimization, the inferior solution may have significant socio-political implications. Since quantum computers cannot be simulated in classical computers, users have no way of verifying the computation outcome. In this paper, we model this adversarial tampering and simulate it's impact on a number of pure quantum and hybrid quantum classical workloads. To guarantee trustworthy computing for a mixture of trusted and untrusted hardware, we propose distributing the total number of shots (i.e., number of repeated execution of a quantum program for computation) equally among the various hardware options. On average, we note ≈ 30X and ≈ 1.5X improvement across the pure quantum workloads and a maximum improvement of ≈ 5X for hybridclassical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive split heuristic leveraging temporal variation in hardware quality to user's advantage, allowing them to identify tampered/untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware, which results in a maximum improvement of ≈ 190X and ≈ 9X across the pure quantum workloads and an improvement of up to ≈ 2.5X for hybrid-classical algorithm.

arXiv (Cornell University), May 3, 2023

Quantum computing is an emerging computing paradigm that can potentially transform several applic... more Quantum computing is an emerging computing paradigm that can potentially transform several application areas by solving some of the intractable problems from classical domain. Similar to classical computing systems, quantum computing stack including software and hardware rely extensively on third parties many of them could be untrusted or lesstrusted or unreliable. Quantum computing stack may contain sensitive Intellectual Properties (IP) that requires protection. From hardware perspective, quantum computers suffer from crosstalk that couples two programs in a multi-tenant setting to facilitate traditionally known fault injection attacks. Furthermore, third party calibration services can report incorrect error rates of qubits or mis-calibrate the qubits to degrade the computation performance for denial-of-service attacks. Quantum computers are expensive and access queue is typically long for trusted providers. Therefore, users may be enticed to explore untrusted but cheaper and readily available quantum hardware which can enable stealth of IP and tampering of quantum programs and/or computation outcomes. Recent studies have indicated the evolution of efficient but untrusted compilation services which presents risks to the IPs present in the quantum circuits. The untrusted compiler can also inject Trojans and perform tampering. Although quantum computing can involve sensitive IP and private information and can solve problems with strategic impact, its security and privacy has received inadequate attention. This paper provides comprehensive overview of the basics of quantum computing, key vulnerabilities embedded in the quantum systems and the recent attack vectors and corresponding defenses. Future research directions are also provided to build a stronger community of quantum security investigators.

Journal of Low Power Electronics and Applications

The size of transistors has drastically reduced over the years. Interconnects have likewise also ... more The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level int...

arXiv (Cornell University), Nov 11, 2022

Quantum computing is changing the way we think about computing. Significant strides in research a... more Quantum computing is changing the way we think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the potential for transformative quantum technology. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments. Besides, a slew of other commercial, government, and academic projects are in the works to create next-generation hardware, a software stack to support the hardware ecosystem, and viable quantum algorithms. This chapter covers various quantum computing architectures including many hardware technologies that are being investigated. We also discuss a variety of challenges, including numerous errors/noise that plague the quantum computers. An overview of literature investigating noise-resilience approaches is also presented.

arXiv (Cornell University), Sep 23, 2022

Quantum computers are currently accessible through a cloud-based platform that allows users to ru... more Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy/untrustworthy/unreliable vendors, to begin offering quantum computers as hardware-as-a-service at various price/performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable/trustworthy hardware. Less-trusted vendors may tamper with the results and/or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of ≈ 40%. To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of ≈30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of ≈45%.

Proceedings of the Great Lakes Symposium on VLSI 2022

Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. Execution of a q... more Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. Execution of a quantum gate in multiple trap TI system may frequently involve ions from two different traps, hence one of the ions needs to be shuttled (moved) between traps to be co-located, degrading fidelity, and increasing the program execution time. The choice of initial mapping influences the number of shuttles. The existing Greedy policy neglects the depth of the program at which a gate is present. Intuitively, the contribution of the late-stage gates to the initial mapping is less since the ions might have already shuttled to a different trap to satisfy other gate operations. In this paper, we target this gap and propose a new program adaptive policy especially for programs with considerable depth and high number of qubits (valid for practical-scale quantum programs). Our technique achieves an average reduction of 9% shuttles/program (with 21.3% at best) for 120 random circuits and enhances the program fidelity up to 3.3X (1.41X on average). CCS CONCEPTS • Hardware → Quantum computation.

arXiv (Cornell University), May 3, 2023

As the quantum computing ecosystem grows in popularity and utility, it is important to identify a... more As the quantum computing ecosystem grows in popularity and utility, it is important to identify and address the security and privacy vulnerabilities before they can be widely exploited. One major concern is the involvement of third-party tools and hardware. As more companies, including those that may be untrusted or unreliable, begin to offer quantum computers as a service, the users will be motivated to use them especially if they are cheaper and readily available compared to trusted ones. This is primarily since the computing time on quantum hardware is expensive and the access queue is typically long. However, usage of untrusted hardware could present the risk of intellectual property (IP) theft. For example, the hybrid quantum classical algorithms like Quantum Approximate Optimization Algorithm (QAOA), that is popular to solve wide range of optimization problems, encodes the graph properties e.g., number of nodes, edges and connectivity in the parameterized quantum circuit to solve a graph maxcut problem. QAOA employs a classical computer which optimizes the parameters of a parametric quantum circuit (which encodes graph structure) iteratively by executing the circuit on a quantum hardware and measuring the output. For mission critical applications like power grid optimization, the graph structure can reveal the power grid and their connectivity (an IP that should be protected). The graph properties can be readily retrieved by analyzing the QAOA circuit by the untrusted quantum hardware provider. To mitigate this risk, we propose an edge pruning obfuscation method for QAOA along with a split iteration methodology. The basic idea is to, (i) create two flavors of QAOA circuit each with few distinct edges eliminated from the problem graph for obfuscation, (ii) iterate the circuits alternately during optimization process to uphold the optimization quality, and (iii) send the circuits to two different untrusted hardware provider so that the adversary has access to partial graph protecting the IP. Extra layers in QAOA circuit are added to recover the optimization quality degradation due to the proposed obfuscation. We demonstrate that combining edge pruning obfuscation with split iteration on two different hardware secures the IP and increases the difficulty of reconstruction while limiting performance degradation to a maximum of 10% (≈ 5% on average) and maintaining low overhead costs (less than 0.5X for QAOA with single layer implementation).

arXiv (Cornell University), May 2, 2023

Security and reliability are primary concerns in any computing paradigm, including quantum comput... more Security and reliability are primary concerns in any computing paradigm, including quantum computing. Currently, users can access quantum computers through a cloud-based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect that more companies including untrusted/less-trusted/unreliable vendors 1 will begin offering quantum computers as hardware-as-a-service at varied price/performance points. Since computing time on quantum hardware is expensive and the access queue could be long, the users will be motivated to use the cheaper and readily available but unreliable/less-trusted hardware. The lesstrusted vendors can tamper with the results, providing a suboptimal solution to the user. For applications such as, critical infrastructure optimization, the inferior solution may have significant socio-political implications. Since quantum computers cannot be simulated in classical computers, users have no way of verifying the computation outcome. In this paper, we model this adversarial tampering and simulate it's impact on a number of pure quantum and hybrid quantum classical workloads. To guarantee trustworthy computing for a mixture of trusted and untrusted hardware, we propose distributing the total number of shots (i.e., number of repeated execution of a quantum program for computation) equally among the various hardware options. On average, we note ≈ 30X and ≈ 1.5X improvement across the pure quantum workloads and a maximum improvement of ≈ 5X for hybridclassical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive split heuristic leveraging temporal variation in hardware quality to user's advantage, allowing them to identify tampered/untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware, which results in a maximum improvement of ≈ 190X and ≈ 9X across the pure quantum workloads and an improvement of up to ≈ 2.5X for hybrid-classical algorithm.

arXiv (Cornell University), May 3, 2023

Quantum computing is an emerging computing paradigm that can potentially transform several applic... more Quantum computing is an emerging computing paradigm that can potentially transform several application areas by solving some of the intractable problems from classical domain. Similar to classical computing systems, quantum computing stack including software and hardware rely extensively on third parties many of them could be untrusted or lesstrusted or unreliable. Quantum computing stack may contain sensitive Intellectual Properties (IP) that requires protection. From hardware perspective, quantum computers suffer from crosstalk that couples two programs in a multi-tenant setting to facilitate traditionally known fault injection attacks. Furthermore, third party calibration services can report incorrect error rates of qubits or mis-calibrate the qubits to degrade the computation performance for denial-of-service attacks. Quantum computers are expensive and access queue is typically long for trusted providers. Therefore, users may be enticed to explore untrusted but cheaper and readily available quantum hardware which can enable stealth of IP and tampering of quantum programs and/or computation outcomes. Recent studies have indicated the evolution of efficient but untrusted compilation services which presents risks to the IPs present in the quantum circuits. The untrusted compiler can also inject Trojans and perform tampering. Although quantum computing can involve sensitive IP and private information and can solve problems with strategic impact, its security and privacy has received inadequate attention. This paper provides comprehensive overview of the basics of quantum computing, key vulnerabilities embedded in the quantum systems and the recent attack vectors and corresponding defenses. Future research directions are also provided to build a stronger community of quantum security investigators.

Journal of Low Power Electronics and Applications

The size of transistors has drastically reduced over the years. Interconnects have likewise also ... more The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level int...

arXiv (Cornell University), Nov 11, 2022

Quantum computing is changing the way we think about computing. Significant strides in research a... more Quantum computing is changing the way we think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the potential for transformative quantum technology. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments. Besides, a slew of other commercial, government, and academic projects are in the works to create next-generation hardware, a software stack to support the hardware ecosystem, and viable quantum algorithms. This chapter covers various quantum computing architectures including many hardware technologies that are being investigated. We also discuss a variety of challenges, including numerous errors/noise that plague the quantum computers. An overview of literature investigating noise-resilience approaches is also presented.

arXiv (Cornell University), Sep 23, 2022

Quantum computers are currently accessible through a cloud-based platform that allows users to ru... more Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy/untrustworthy/unreliable vendors, to begin offering quantum computers as hardware-as-a-service at various price/performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable/trustworthy hardware. Less-trusted vendors may tamper with the results and/or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of ≈ 40%. To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of ≈30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of ≈45%.

Proceedings of the Great Lakes Symposium on VLSI 2022

Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. Execution of a q... more Trapped-ion (TI) quantum computer is one of the forerunner quantum technologies. Execution of a quantum gate in multiple trap TI system may frequently involve ions from two different traps, hence one of the ions needs to be shuttled (moved) between traps to be co-located, degrading fidelity, and increasing the program execution time. The choice of initial mapping influences the number of shuttles. The existing Greedy policy neglects the depth of the program at which a gate is present. Intuitively, the contribution of the late-stage gates to the initial mapping is less since the ions might have already shuttled to a different trap to satisfy other gate operations. In this paper, we target this gap and propose a new program adaptive policy especially for programs with considerable depth and high number of qubits (valid for practical-scale quantum programs). Our technique achieves an average reduction of 9% shuttles/program (with 21.3% at best) for 120 random circuits and enhances the program fidelity up to 3.3X (1.41X on average). CCS CONCEPTS • Hardware → Quantum computation.

arXiv (Cornell University), May 3, 2023

As the quantum computing ecosystem grows in popularity and utility, it is important to identify a... more As the quantum computing ecosystem grows in popularity and utility, it is important to identify and address the security and privacy vulnerabilities before they can be widely exploited. One major concern is the involvement of third-party tools and hardware. As more companies, including those that may be untrusted or unreliable, begin to offer quantum computers as a service, the users will be motivated to use them especially if they are cheaper and readily available compared to trusted ones. This is primarily since the computing time on quantum hardware is expensive and the access queue is typically long. However, usage of untrusted hardware could present the risk of intellectual property (IP) theft. For example, the hybrid quantum classical algorithms like Quantum Approximate Optimization Algorithm (QAOA), that is popular to solve wide range of optimization problems, encodes the graph properties e.g., number of nodes, edges and connectivity in the parameterized quantum circuit to solve a graph maxcut problem. QAOA employs a classical computer which optimizes the parameters of a parametric quantum circuit (which encodes graph structure) iteratively by executing the circuit on a quantum hardware and measuring the output. For mission critical applications like power grid optimization, the graph structure can reveal the power grid and their connectivity (an IP that should be protected). The graph properties can be readily retrieved by analyzing the QAOA circuit by the untrusted quantum hardware provider. To mitigate this risk, we propose an edge pruning obfuscation method for QAOA along with a split iteration methodology. The basic idea is to, (i) create two flavors of QAOA circuit each with few distinct edges eliminated from the problem graph for obfuscation, (ii) iterate the circuits alternately during optimization process to uphold the optimization quality, and (iii) send the circuits to two different untrusted hardware provider so that the adversary has access to partial graph protecting the IP. Extra layers in QAOA circuit are added to recover the optimization quality degradation due to the proposed obfuscation. We demonstrate that combining edge pruning obfuscation with split iteration on two different hardware secures the IP and increases the difficulty of reconstruction while limiting performance degradation to a maximum of 10% (≈ 5% on average) and maintaining low overhead costs (less than 0.5X for QAOA with single layer implementation).