[Paper Review] Optimizing Noisy-Intermediate Scale Quantum Circuits: A Block-Based Synthesis

Xin-Chuan Wu, Marc Davis+2 more|arXiv (Cornell University)|Dec 17, 2020

Quantum Computing Algorithms and Architecture40 references4 citations

TL;DR

This paper introduces QGo, a block-based quantum circuit optimization framework that partitions large circuits into independent blocks, re-synthesizes each block using quantum synthesis to minimize CNOT gates, and re-composes the circuit. It reduces CNOT count by 29.9% on average (up to 50%) compared to industrial compilers like t|ket>, enabling improved fidelity and scalability to 60+ qubit circuits.

ABSTRACT

The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block optimization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noise simulations, and large-size circuits on analytical models. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compilers such as t|ket>. When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits. Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation toolchain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains.

Motivation & Objective

• To address the exponential cost of quantum circuit synthesis on large-scale NISQ devices.
• To reduce the number of noisy two-qubit gates, particularly CNOTs, to improve circuit fidelity.
• To enable scalable optimization of large circuits by decomposing them into manageable, independent blocks.
• To integrate synthesis into production compiler toolchains for practical use in NISQ-era quantum computing.

Proposed method

• Partitioning the input quantum circuit into a sequence of independent circuit blocks based on structural and functional criteria.
• Re-generating and optimizing each block using quantum circuit synthesis to minimize CNOT gate count.
• Re-composing the final circuit by stitching the optimized blocks together while preserving functional equivalence.
• Applying the framework across three regimes: real devices (IBM Athens), noise simulations, and analytical models for scalability.
• Leveraging hierarchical optimization to scale exponential-cost synthesis to large circuits (60+ qubits).
• Validating the approach using a benchmark suite of NISQ circuits and comparing against industrial compilers like t|ket>.

Experimental results

Research questions

• RQ1 Can block-based decomposition enable scalable quantum circuit optimization despite the exponential cost of full-circuit synthesis?
• RQ2 To what extent can CNOT gate count be reduced using block-level synthesis while maintaining circuit fidelity?
• RQ3 How does the proposed optimization perform on real hardware, noise-simulated circuits, and large-scale analytical models?
• RQ4 Can the framework be effectively integrated into production compiler toolchains for industrial use?

Key findings

• QGo reduces the number of CNOT gates by an average of 29.9% across NISQ benchmarks compared to t|ket>.
• In some cases, QGo achieves up to a 50% reduction in CNOT gate count relative to industrial compilers.
• On the IBM Athens system, shorter circuits resulting from QGo optimization lead to higher measured circuit fidelity.
• The framework successfully scales to optimize circuits with 60 or more qubits, demonstrating feasibility for large-scale NISQ applications.
• QGo is the first technique to successfully employ and scale quantum circuit synthesis in a production compiler toolchain.