While the prominent quantum computing architectures are based on superconducting technology, new quantum hardware technologies are emerging, such as Trapped Ions, Neutral Atoms (or FPQAs), Silicon Spin Qubits, etc. This diverse set of technologies presents fundamental trade-offs in terms of scalability, performance, manufacturing, and operating expenses. To manage these diverse quantum technologies, there is a growing need for a retargetable compiler that can efficiently adapt existing code to these emerging hardware platforms. Such a retargetable compiler must be extensible to support new and rapidly evolving technologies, performant with fast compilation times and high-fidelity execution, and verifiable through rigorous equivalence checking to ensure the functional equivalence of the retargeted code. To this end, we present Weaver, the first extensible, performant, and verifiable retargetable quantum compiler framework with a focus on FPQAs due to their unique, promising features. Weaver introduces wQasm, the first formal extension of the standard OpenQASM quantum assembly with FPQA-specific instructions to support their distinct capabilities. Next, Weaver implements the wOptimizer, an extensible set of FPQA-specific optimization passes to improve execution quality. Last, the wChecker automatically checks for equivalence between the original and the retargeted code. Our evaluation shows that Weaver improves compilation times by 103×, execution times by 4.4×, and execution fidelity by 10%, on average, compared to superconducting and state-of-the-art (non-retargetable) FPQA compilers.

Today's modern hardware, with manycore servers, RDMA-capable networks and trusted execution environments, challenges the conventional wisdom about CFT protocols' design. We explore the synergy between modern hardware and the security and performance of strongly consistent replication protocols. Specifically, can we leverage (and how) modern cloud hardware to harden the security properties of a CFT protocol for Byzantine settings while achieving high performance?

We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor abstracts away the complexity of hybrid programming and resource management by exposing the Qonductor API, a high-level and hardware-agnostic API. The resource estimator strategically balances quantum and classical resources to mitigate resource contention and the effects of hardware noise. The hybrid scheduler automates job scheduling on hybrid resources and balances the tradeoff between users’ objectives of QoS and the cloud operator’s objective of resource efficiency.

We present the Quantum Gate Virtualization Machine (QVM), an end-to-end generic system for scalable execution of large quantum circuits with high fidelity on noisy and small quantum processors (QPUs) by leveraging gate virtualization. QVM exposes a virtual circuit intermediate representation (IR) that extends the notion of quantum circuits to incorporate gate virtualization. Based on the virtual circuit as our IR, we propose the QVM compiler—an extensible compiler infrastructure to transpile a virtual circuit through a series of modular optimization passes to produce a set of optimized circuit fragments. Lastly, these transpiled circuit fragments are executed on QPUs using our QVM runtime—a scalable and parallel infrastructure to virtualize and execute circuit fragments on a set of QPUs. We evaluate QVM on IBM’s 7- and 27-qubit QPUs. Our evaluation shows that our approach allows for the execution of circuits with up to double the number of qubits compared to the qubit-count of a QPU, while improving fidelity by 4.7 × on average compared to larger QPUs and that we can effectively reduce circuit depths to only 40 % of the original circuit depths.

Distributed datastores underpin highly concurrent, read-intensive applications, ensuring consistency, availability, and performance. They use crash-tolerant protocols to replicate data and endure replica server crashes. To ensure safety and meet the performance demands, replication must support high-throughput, strongly consistent (i.e., linearizable) reads without assuming any synchrony. However, existing protocols either 1 relax consistency, or provide linearizable reads that are 2 fully asynchronous but remote (involving multiple replicas), or 3 local but require synchrony. This work explores the tradeoffs between consistency, asynchrony, and performance in crash-tolerant protocols, and proves that in linearizable asynchronous read/write registers tolerating a single crash, no reads can be local. Building on this, we introduce almost-local reads (ALRs), a new abstraction that ensures crash tolerance and linearizability under asynchrony. While ALRs have slightly higher latency than local reads, they remain lightweight, with computation and network costs close to single-node reads. We present two simple yet effective ALR schemes that enhance protocols across all three categories. For protocols with local reads, ALRs address consistency or synchrony issues with minimal throughput loss. In asynchronous linearizable protocols, they improve performance without compromises. Our evaluation shows that ALR-enhanced ZAB and Hermes achieve within 2% and 5% of their original throughput in 95% reads while ensuring linearizability under asynchrony. On Raft, ALRs deliver over 2.5x higher throughput without compromising consistency or asynchrony.

Stateful serverless applications need to persist their state and data. The existing approach is to store the data in general purpose storage systems. However, these approaches are not designed to meet the demands of serverless applications in terms of consistency, fault tolerance and performance.