We present a compiler for programming quantum architectures based on the Quantum Random Access Machine (QRAM) model. The QRAM model consists of a classical subsystem responsible for generating the quantum operations that are executed on a quantum subsystem. The compiler can also be applied to trade studies for optimizing the reliability and latency of quantum programs and to determine the required error correction resources. We use the Bacon-Shor [9, 1, 3] quantum error correcting code as an example quantum program that can be processed and analyzed by the compiler.
We present a compiler for programming quantum architectures based on the Quantum Random Access Machine (QRAM) model. The QRAM model consists of a classical subsystem responsible for generating the quantum operations that are executed on a quantum subsystem. The compiler can also be applied to trade studies for optimizing the reliability and latency of quantum programs and to determine the required error correction resources. We use the Bacon-Shor [9, 1, 3] quantum error correcting code as an example quantum program that can be processed and analyzed by the compiler.
We present the SQRAM architecture for quantum computing, which is based on Knill's QRAM model. We detail a suitable instruction set, which implements a universal set of quantum gates, and demonstrate the operation of the SQRAM with Deutsch's quantum algorithm. The compilation of high-level quantum programs for the SQRAM machine is considered; we present templates for quantum assembly code and a method for decomposing matrices for complex quantum operations. The SQRAM simulator and compiler are discussed, along with directions for future work.
As quantum computing advances toward fault-tolerant architectures, quantum error detection (QED) has emerged as a practical and scalable intermediate strategy in the transition from error mitigation to full error correction. By identifying and discarding faulty runs rather than correcting them, QED enables improved reliability with significantly lower overhead. Applying QED to arbitrary quantum circuits remains challenging, however, because of the need for manual insertion of detection subcircuits, ancilla allocation, and hardware-specific mapping and scheduling. We present QuantEM, a modular and extensible compiler designed to automate the integration of QED codes into arbitrary quantum programs. Our compiler consists of three key modules: (1) program analysis and transformation module to examine quantum programs in a QED-aware context and introduce checks and ancilla qubits, (2) error detection code integration module to map augmented circuits onto specific hardware backends, and (3) postprocessing and resource management for measurement results postprocessing and resource-efficient estimation techniques. The compiler accepts a high-level quantum circuit, a chosen error detection code, and a target hardware topology and then produces an optimized and executable circuit. It can also automatically select an appropriate detection code for the user based on circuit structure and resource estimates. QuantEM currently supports Pauli check sandwiching and Iceberg codes and is designed to support future QED schemes and hardware targets. By automating the complex QED compilation flow, this work reduces developer burden, enables fast code exploration, and ensures consistent and correct application of detection logic across architectures.
Practical realizations of quantum computers are poised to deliver outstanding computational capabilities far beyond the reach of any classical supercomputer. While classical systems are reliably implemented using CMOS technology, the fabrication of quantum hardware has not yet reached a commercially viable level of maturity. Nowadays, many different technologies are being developed as competing candidates to build the first error-corrected quantum computer. Among the software resources required to operate such a system, quantum compilers translate a high-level description of a quantum algorithm into low-level, technology-dependent quantum instructions. Many quantum algorithms, including Shor's and Grover's algorithms, require to compute some classical logic functions on the quantum computer. The first part of this thesis deals with the problem of compiling quantum circuits that perform such classical Boolean functions, called oracles, into a fault-tolerant set of instructions. Oracle circuits often demand many resources in terms of the number of qubits and gates. The focus is on compiling quantum circuits starting from multi-level logic networks representing large Boolean functions while minimizing the resource footprint of the obtained quantum circuits. At the same time, the trade-off between memory resources and the number of operations typical of such a compilation process is explored. As part of the effort in reducing the resources required to perform a given quantum program, I also present some quantum circuit optimization techniques. The researched algorithms leverage data structures and techniques borrowed and adapted from classical logic synthesis, e.g., SAT solvers, LUT mapping, and multi-level logic networks. I implemented all the compilation algorithms presented in this thesis as part of open-source projects. In particular, I develop and maintain caterpillar---a C++ header-only library dedicated to the quantum compilation of oracles and quantum memory management. The second part of this thesis describes how to equip quantum programming language compilers with automatic accuracy management. Despite the availability of many of such languages, resource estimation of quantum algorithms does not yet support taking approximation errors into account. This general methodology is applicable to any programming language and I demonstrate its integration into the Q# compiler. The technique consists of providing language support to ease the accuracy-aware programming task. The user can define accuracy parameters that will be automatically optimized according to a constraint and a cost function directly generated from the source code. In the presented practical evaluations, the constraint function is the overall allowed accuracy, while the cost function is application-dependent and related to the number of gates. During the optimization process, such functions are evaluated hundreds of times. For this reason, we extract them as (near-)symbolic expressions, whose evaluation time does not depend on the quantum algorithm size. The algorithms and the methodologies presented in this thesis are part of a widespread effort of the research community to build a complete and efficient software stack to program and control the first practical universal quantum computer.
A random access memory, or RAM, is a device that, when interrogated, returns the content of a memory location in a memory array. A quantum RAM, or qRAM, allows one to access superpositions of memory sites, which may contain either quantum or classical information. RAMs and qRAMs with n-bit addresses can access 2n memory sites. Any design for a RAM or qRAM then requires O(2n) two-bit logic gates. At first sight this requirement might seem to make large scale quantum versions of such devices impractical, due to the difficulty of constructing and operating coherent devices with large numbers of quantum logic gates. Here we analyze two different RAM architectures (the conventional fanout and the ``bucket brigade'') and propose some proof-of-principle implementations, which show that, in principle, only O(n) two-qubit physical interactions need take place during each qRAM call. That is, although a qRAM needs O(2n) quantum logic gates, only O(n) need to be activated during a memory call. The resulting decrease in resources could give rise to the construction of large qRAMs that could operate without the need for extensive quantum error correction.
Quantum computation has advanced to the point where system-level solutions can help close the gap between emerging quantum technologies and real-world computing requirements. Empirical studies of practical quantum architectures are just beginning to appear in the literature. Elementary architectural concepts are still lacking: How do we provide quantum storage, data paths, classical control circuits, parallelism, and system integration? And, crucially, how can we design architectures to reduce error-correction overhead? The authors describe a proposed architecture that uses code teleportation, quantum memory refresh units, dynamic compilation of quantum programs, and scalable error correction to achieve system-level efficiencies. They assert that their work indicates the underlying technology's reliability is crucial; practical architectures will require quantum technologies with error rates between 10/sup -6/ and 10/sup -9/.
Compiling quantum programs for fault-tolerant execution requires transforming high-level operations through multiple abstraction layers: from logical gates to error-corrected encodings to hardware-native instructions. A key challenge is that quantum error correction turns purely quantum programs into hybrid quantum-classical programs, where classical feedback from syndrome measurements drives quantum corrections at runtime. Existing compilation frameworks handle these quantum and classical components separately, requiring manual adaptation of classical logic at each compilation stage, all while preserving program semantics. We present qstack, a compiler framework built around a purely quantum intermediate representation in which classical logic is accessed only through opaque callbacks, written in any classical language. The framework's central mechanism, callback wrapping, enables compositional compilation: each compiler pass automatically adapts both quantum operations and their associated classical callbacks, and any kernel dynamically generated by a callback is compiled through the full pipeline. This allows ISA translation and quantum error correction to be expressed as composable compiler passes, including concatenation of error-correcting codes, without manual intervention. We demonstrate end-to-end compilation from a high-level gate set through Clifford gates to trapped-ion native operations, with bit-flip and phase-flip repetition codes, the Steane code, and the Shor code obtained by composing two repetition passes.