In this work, we introduce “Quantum-Chiplet”, a novel design flow for quantum circuits via a VLSI-like methodology. A large-scale quantum problem is hierarchically decomposed into several functions, or “chiplets,” and designed one by one. Each “chiplet” is initially represented with a behavior model without the need for quantum gate-level details, allowing for greater flexibility in designing arbitrary digital logic under any qubit count within a quantum environment. Focusing on behavior-level design, we also shorten the simulation time by more than an order of magnitude, as demonstrated by comparing the simulation time of QuantumChiplet to the conventional quantum simulator, Qiskit. To synthesize the design into real quantum gates, we leverage the conventional VLSI tool–Design Compiler for the automatic generation of quantum-gate-level design and automatically translate it into code interpretable by conventional quantum simulators. Moreover, Quantum-Chiplet utilizes matrices and the Python NumPy library for simulation. So even if suitable quantum gates are hard to find in existing libraries, simulations can still be done. These advancements, from bottom to top, pave the way for efficiently simulating and scaling up quantum circuits, and offer insight into electronic design automation for next-generation fault-tolerant quantum computing.
In this work, we introduce “Quantum-Chiplet”, a novel design flow for quantum circuits via a VLSI-like methodology. A large-scale quantum problem is hierarchically decomposed into several functions, or “chiplets,” and designed one by one. Each “chiplet” is initially represented with a behavior model without the need for quantum gate-level details, allowing for greater flexibility in designing arbitrary digital logic under any qubit count within a quantum environment. Focusing on behavior-level design, we also shorten the simulation time by more than an order of magnitude, as demonstrated by comparing the simulation time of QuantumChiplet to the conventional quantum simulator, Qiskit. To synthesize the design into real quantum gates, we leverage the conventional VLSI tool–Design Compiler for the automatic generation of quantum-gate-level design and automatically translate it into code interpretable by conventional quantum simulators. Moreover, Quantum-Chiplet utilizes matrices and the Python NumPy library for simulation. So even if suitable quantum gates are hard to find in existing libraries, simulations can still be done. These advancements, from bottom to top, pave the way for efficiently simulating and scaling up quantum circuits, and offer insight into electronic design automation for next-generation fault-tolerant quantum computing.
Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2^n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits.
This paper proposes a VLSI-like design and verification flow for quantum circuit implementation, covering four levels: behavioral modeling, register-transfer level (RTL), gate level, and full-circuit integration with quantum algorithms. Using quantum Monte Carlo methods for pi estimation as a case study, the proposed flow provides a systematic path from initial specifications to large-scale quantum circuit implementation. The paper highlights two key contributions. First, quantum digital design supports a broad range of applications beyond predefined circuit templates. This capability is particularly important for application-specific operations in algorithms such as Shor's, Grover's, and quantum amplitude estimation (QAE), and can significantly advance fault-tolerant quantum computing (FTQC). Second, classical simulation shows that replacing random sampling with grid sampling can achieve a quadratic improvement in this pi-estimation case without quantum computation. This suggests that the quadratic speedup may arise from the grid sampling strategy rather than from the QAE circuit itself. By adapting conventional VLSI digital design principles, this four-level flow addresses a key bottleneck in quantum circuit design and enables flexible design initiation from multiple entry points, including specifications, RTL, and gate-level descriptions, paving the way for the practical deployment of quantum computing.
2-local qubit Hamiltonian simulation, a fundamental task in quantum computing, is widely applied in various applications. This paper presents QBX, the first quantum compiler designed for 2-local qubit Hamiltonian simulation on quantum chiplet architectures. Existing general-purpose quantum compilers for chiplet architectures target quantum programs at the gate level and miss optimizations requiring high-level semantics of Hamiltonian simulation. Conversely, existing domain-specific compilers for Hamiltonian simulation are designed for monolithic architectures, and their limited scalability and inability to adapt to the heterogeneity of chiplet architectures lead to sub-optimal circuit outputs. QBX implements a scalable hierarchical approach to reduce both the amount and distance of cross-chiplet communications. It integrates the highway mechanism, a chiplet-oriented solution for efficient long-range qubit communication, to diminish cross-chiplet interaction costs. Furthermore, QBX efficiently aggregates 2-local Pauli strings into multi-target controlled gates, harnessing highways for deep optimization. Our evaluations show that QBX markedly outperforms both general-purpose and domain-specific compilers in circuit depth and operation count, while scaling to much larger 2-local Hamiltonian simulations.
As quantum computing advances toward fault-tolerance through quantum error correction, modular chiplet architectures have emerged to provide the massive qubit counts required while overcoming fabrication limits of monolithic chips. However, this transition introduces a critical compilation gap: existing frameworks cannot handle the scale of fault-tolerant quantum circuits while managing the noisy, sparse interconnects of chiplet backends.
We present Chipmunq, the first hardware-aware compiler for mapping and routing fault-tolerant circuits onto modular architectures. Chipmunq employs a quantum-error-correction-aware partitioning strategy that preserves the integrity of logical qubit patches, preventing prohibitive gate overheads common in general-purpose compilers. Our evaluation demonstrates that Chipmunq achieves a 13.5x speedup in compilation time compared to state-of-the-art tools. By incorporating chiplet constraints and defective qubits, it reduces circuit depth by 86.4% and SWAP gate counts by 91.4% across varying code distances. Crucially, Chipmunq overcomes heterogeneous inter-chiplet links, improving logical error rates by up to two orders of magnitude.
Although the concept of quantum computing has existed for decades, the technology needed to successfully implement a quantum computing system has not yet reached the level of sophistication, reliability, and scalability necessary for commercial viability until very recently. Significant progress on this front was made in the past few years, with IBM planning to create a 1000-qubit chip by the end of 2023, and Google already claiming to have achieved quantum supremacy. Other major industry players such as Intel and Microsoft have also invested significant amounts of resources into quantum computing research. Any viable computing system requires both hardware and software to work together harmoniously in order to perform useful computations. While the achievements of IBM and other companies represent a large step forward for quantum hardware, many gaps remain to be filled with respect to the corresponding software. Specifically, there is currently no clear path towards a complete process for translating quantum algorithms into physical operations that are directly executable on quantum hardware. Such a process is analogous to a compiler that translates programs written in a high-level language into executable machine instructions on a conventional digital computer, and it is necessary if quantum computers are to be harnessed to perform practically useful computations. Existing work has addressed individual components of this process, but so far no unified method for translating the whole of a quantum algorithm into executable operations has been described. I make substantial progress towards filling this gap by describing a set of high-level and low-level quantum circuit design techniques, which when taken together reduce the need of a circuit designer to be concerned with low-level details. On the high-level side, I describe an approach or strategy to designing quantum oracles for Grover's algorithm that allows it to be applied to several types of problems. This approach involves designing oracles in terms of high-level blocks such as counters, multiplexers, comparators, and arbitrary Boolean functions. The implementations of these blocks in terms of lower-level quantum gates are demonstrated in a way that makes it clear that scaled-up versions of them can be generated in a completely automated fashion. For a specific class of problems, which I call state-space path planning problems, I also introduce a paradigm for quantum oracle design that involves representing the problem in terms of individual states and moves. Problems of this sort have applications in robotics and games. Low-level techniques that I introduce
Quantum mechanics based computing systems are expected to have high capabilities and are considered good candidates to replace classical cryptography and supercomputing systems. Among many implementations, quantum optics systems provide a promising platform to implement universal quantum computers, since they link quantum computation and quantum communication in the same framework. Recently, several quantum gates, circuits, and protocols have been experimentally realized using optics. Despite the fact that big advances in building the physical quantum computers were achieved, there are no currently available industrial computer aided tools that can perform the modeling, analysis, and verification of optical quantum computing systems. In this thesis, we tackle the idea of design automation for quantum circuits, where we use a sound language, higher order logic, to model and reason about quantum circuits formally. In particular, we propose a framework for the hierarchical modeling and automated verification of quantum computing circuits. The modeling approach captures quantum models built hierarchically from quantum gates, which models are readily available in a library. The analysis and verification of composed circuits is done seamlessly based on dedicated mathematical foundations formalized in the theorem prover. \nSpecifically, the tensor product and linear projection are used to extract the quantum circuit outputs. Subsequently, a rich library of quantum gates which includes 1-qubit, 2-qubit, and 3-qubit gates is formalized. In order to automate the analysis process, we developed a decision procedure to eliminate the need of user guidance throughout the formal proofs. To demonstrate the effectiveness of the proposed framework, we conduct the formal analysis of a benchmark of quantum circuits including the Shor's integer factorization algorithm, the Grover's oracle, and the quantum full adder.