A software methodology for compiling quantum programs

Häner, Thomas; Steiger, Damian S.; Svore, Krysta M.; Troyer, Matthias

doi:10.1088/2058-9565/aaa5cc

Cited by 143 publications

(148 citation statements)

References 33 publications

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“…Quantum compiling [25][26][27] is one of these applications. Compiling refers to transforming a high-level algorithm into a low-level machine code.…”

Section: Introductionmentioning

confidence: 99%

Noise resilience of variational quantum compiling

et al. 2020

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Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the costevaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters are unaffected by a broad class of noise models, such as measurement noise, gate noise, and Pauli channel noise. Furthermore, our numerical implementations on IBM's noisy simulator demonstrate resilience when compiling the quantum Fourier transform, Toffoli gate, and W-state preparation. Hence, variational quantum compiling, due to its robustness, could be practically useful for noisy intermediate-scale quantum devices. Finally, we speculate that this noise resilience may be a general phenomenon that applies to other VHQCAs such as the variational quantum eigensolver.

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“…Quantum compiling [25][26][27] is one of these applications. Compiling refers to transforming a high-level algorithm into a low-level machine code.…”

Section: Introductionmentioning

confidence: 99%

Noise resilience of variational quantum compiling

et al. 2020

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show abstract

“…It may not be obvious how to optimize algorithms for a given connectivity and a given gate set. This motivates the idea of an automated approach for discovering and optimizing quantum algorithms [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Learning the quantum algorithm for state overlap

et al. 2018

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Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap rs ( ) Tr between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ=σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error-compared to the Swap Test-on these computers.

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“…The last few years have seen an explosion of interest in quantum programming languages, and the problems of quantum compilation have been explored at various levels of abstraction [26], from high-level algorithm design to pulse control at the machine level.…”

Section: Related Workmentioning

confidence: 99%

“…In keeping with its focus on NISQ devices, the design of t|ket is minimalistic compared to the schema proposed by Häner et al [26]. There is no error correction, and t|ket does not include a linker, preferring to rely on application programming frameworks such as CQC's Eumen or IBM's Qiskit Aqua to provide libraries of common routines.…”

Section: System Overviewmentioning

confidence: 99%

t|ket⟩: a retargetable compiler for NISQ devices

Sivarajah

Dilkes

Cowtan

et al. 2020

Quantum Sci. Technol.

300

238

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We present t|ket , a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t|ket is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing. User Runtime GPU Classical HPC Logging Task Manager Scheduler Real-Time Controller Control Device Control Device Readout Device … Figure 2: Idealised system architecture for a NISQ Computerprogrammable devices which drive the evolution of the qubits and read out their states. An example of this kind of device is an arbitrary waveform generator, as found in many superconducting architectures. The microwave pulse sequences output by these devices are generated by simple low-level programs optimised for speed of execution. These devices, and the real-time controller which synchronises them, operate in a hard real-time environment where the computation takes place on the time-scale of the coherence time of the qubits. These components combine to execute a single instance of a quantum circuit, possibly with some classical control. By analogy with GPU computing, we refer to this layer as a kernel. One level higher, the scheduler is responsible for dispatching circuits to be run and packaging the results for the higher layers. It is also likely to be heavily involved in the device calibration process. (Calibration data are an important input to the compiler.) This layer and those below may be thought of as the low-level system software of the quantum computer, and must normally be physically close to the device. In the layer above we find service-oriented middleware, principally the task manager, which may distribute jobs to different quantum devices or simulators, GPUs, and perhaps conventional HPC resources, to perform the various subroutines of the quantum algorithm. This layer may also allocate access to the quantum system among multiple users. Finally, at the highest level is the user runtime, which defines the overall algorithm and integrates the results of the subcomputations to produce the final answer.

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A software methodology for compiling quantum programs

Cited by 143 publications

References 33 publications

Noise resilience of variational quantum compiling

Noise resilience of variational quantum compiling

Learning the quantum algorithm for state overlap

t|ket⟩: a retargetable compiler for NISQ devices

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