Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures

Quantum computers have recently made great strides and are on a long-term path towards useful fault-tolerant computation. A dominant overhead in fault-tolerant quantum computation is the production of high-fidelity encoded qubits, called magic states, which enable reliable error-corrected computation. We present the first detailed designs of hardware functional units that implement space-time optimized magic-state factories for surface code error-corrected machines. Interactions among distant qubits require surface code braids (physical pathways on chip) which must be routed. Magic-state factories are circuits comprised of a complex set of braids that is more difficult to route than quantum circuits considered in previous work [1]. This paper explores the impact of scheduling techniques, such as gate reordering and qubit renaming, and we propose two novel mapping techniques: braid repulsion and dipole moment braid rotation. We combine these techniques with graph partitioning and community detection algorithms, and further introduce a stitching algorithm for mapping subgraphs onto a physical machine. Our results show a factor of 5.64 reduction in space-time volume compared to the best-known previous designs for magic-state factories.Comment: 13 pages, 10 figure

Resource Optimized Quantum Architectures for Surface Code Implementations of Magic-State Distillation

Quantum computers capable of solving classically intractable problems are under construction, and intermediate-scale devices are approaching completion. Current efforts to design large-scale devices require allocating immense resources to error correction, with the majority dedicated to the production of high-fidelity ancillary states known as magic-states. Leading techniques focus on dedicating a large, contiguous region of the processor as a single "magic-state distillation factory" responsible for meeting the magic-state demands of applications. In this work we design and analyze a set of optimized factory architectural layouts that divide a single factory into spatially distributed factories located throughout the processor. We find that distributed factory architectures minimize the space-time volume overhead imposed by distillation. Additionally, we find that the number of distributed components in each optimal configuration is sensitive to application characteristics and underlying physical device error rates. More specifically, we find that the rate at which T-gates are demanded by an application has a significant impact on the optimal distillation architecture. We develop an optimization procedure that discovers the optimal number of factory distillation rounds and number of output magic states per factory, as well as an overall system architecture that interacts with the factories. This yields between a 10x and 20x resource reduction compared to commonly accepted single factory designs. Performance is analyzed across representative application classes such as quantum simulation and quantum chemistry.Comment: 16 pages, 14 figure

Hardware Architecture for a Quantum Computer Trusted Execution Environment

The cloud-based environments in which today's and future quantum computers will operate, raise concerns about the security and privacy of user's intellectual property. Quantum circuits submitted to cloud-based quantum computer providers represent sensitive or proprietary algorithms developed by users that need protection. Further, input data is hard-coded into the circuits, and leakage of the circuits can expose users' data. To help protect users' circuits and data from possibly malicious quantum computer cloud providers, this work presented the first hardware architecture for a trusted execution environment for quantum computers. To protect the user's circuits and data, the quantum computer control pulses are obfuscated with decoy control pulses. While digital data can be encrypted, analog control pulses cannot and this paper proposed the novel decoy pulse approach to obfuscate the analog control pulses. The proposed decoy pulses can easily be added to the software by users. Meanwhile, the hardware components of the architecture proposed in this paper take care of eliminating, i.e. attenuating, the decoy pulses inside the superconducting quantum computer's dilution refrigerator before they reach the qubits. The hardware architecture also contains tamper-resistant features to protect the trusted hardware and users' information. The work leverages a new metric of variational distance to analyze the impact and scalability of hardware protection. The variational distance of the circuits protected with our scheme, compared to unprotected circuits, is in the range of only $0.16$ to $0.26$. This work demonstrates that protection from possibly malicious cloud providers is feasible and all the hardware components needed for the proposed architecture are available today

SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via Cost-Effective Uncomputation

Compiling high-level quantum programs to machines that are size constrained (i.e. limited number of quantum bits) and time constrained (i.e. limited number of quantum operations) is challenging. In this paper, we present SQUARE (Strategic QUantum Ancilla REuse), a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in modular quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse. Current Noisy Intermediate-Scale Quantum (NISQ) computers and forward-looking Fault-Tolerant (FT) quantum computers have fundamentally different constraints such as data locality, instruction parallelism, and communication overhead. Our heuristic-based ancilla-reuse algorithm balances these considerations and fits computations into resource-constrained NISQ or FT quantum machines, throttling parallelism when necessary. To precisely capture the workload of a program, we propose an improved metric, the "active quantum volume," and use this metric to evaluate the effectiveness of our algorithm. Our results show that SQUARE improves the average success rate of NISQ applications by 1.47X. Surprisingly, the additional gates for uncomputation create ancilla with better locality, and result in substantially fewer swap gates and less gate noise overall. SQUARE also achieves an average reduction of 1.5X (and up to 9.6X) in active quantum volume for FT machines.Comment: 14 pages, 10 figure

Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers

Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states for errors. The syndromes are subsequently interpreted by a decoding algorithm to identify and correct errors in the data qubits. This task is complex due to the multiplicity of error sources affecting both data and syndrome qubits as well as syndrome extraction operations. Additionally, identical syndromes can emanate from different error sources, necessitating a decoding algorithm that evaluates syndromes collectively. Although machine learning (ML) decoders such as multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) have been proposed, they often focus on local syndrome regions and require retraining when adjusting for different code distances. We introduce a transformer-based QEC decoder which employs self-attention to achieve a global receptive field across all input syndromes. It incorporates a mixed loss training approach, combining both local physical error and global parity label losses. Moreover, the transformer architecture's inherent adaptability to variable-length inputs allows for efficient transfer learning, enabling the decoder to adapt to varying code distances without retraining. Evaluation on six code distances and ten different error configurations demonstrates that our model consistently outperforms non-ML decoders, such as Union Find (UF) and Minimum Weight Perfect Matching (MWPM), and other ML decoders, thereby achieving best logical error rates. Moreover, the transfer learning can save over 10x of training cost.Comment: Accepted to ICCAD 2023, FAST ML for Science Workshop; 7 pages, 8 figure

Refractory microsatellite stable metastatic colorectal cancer with ERBB2/ERBB3 mutation may be preferred population for regorafenib plus PD-1 inhibitor therapy: a real-world study

BackgroundMicrosatellite stable (MSS) colorectal cancer (CRC) has been referred to as the “cold tumor” because of almost no response to anti–programmed death-1 (PD-1) antibody. A recent REGONIVO trial showed that regorafenib plus nivolumab had an encouraging efficacy in MSS metastatic CRC (mCRC). However, only a small subset of patients may benefit from the combination therapy. We aim to evaluate the efficacy and safety data of immune checkpoint inhibitors combined with regorafenib in refractory MSS mCRC and to discover biomarkers that can effectively stratify the beneficial patient population.MethodsWe retrospectively analyzed patients with MSS mCRC who received regorafenib combined with anti–PD-1 antibody therapy. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and status of gene mutation were reviewed and evaluated.ResultsTwenty-one patients received combination treatment. At a median treatment duration of 4 months, one patient achieved complete response, three patients achieved partial response, and two patients achieved stable disease as the best response. The ORR and DCR were 19% and 28.5% in the overall population, respectively. The median PFS was 4 months, and the median OS was 25 months. Only erbb2 receptor tyrosine kinase 2/erbb3 receptor tyrosine kinase 3 (ERBB2/ERBB3) mutation status was confirmed to be a potential predictive factor for effective treatment. In patients with ERBB2/ERBB3 mutation, ORR, DCR, and PFS exhibited significant improvements in comparison with that in wild-type patients. Grade 3 or higher treatment-related adverse events occurred in three patients (14.3%).ConclusionsRegorafenib in combination with PD-1 inhibitor provides a feasible treatment regimen for refractory MSS mCRC with tolerated toxicity. Patients with ERBB2/ERBB3 mutation may be the preferred population for this combination regimen