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This part explains the design and usage rules of oracq. The recommended reading order starts with operations, registers, and oracles, then moves on to algorithms and domain applications. Each chapter can be consulted independently; detailed object definitions are in the API reference.

• Installation and environment
  • Optional backends
  • Building the documentation
• Operations, registers, and the generation process
  • Registers and views
  • Modules and unfinished implementations
  • Precision and mathematical assumptions
• Source code structure
  • Infrastructure
  • Algorithm library
  • Domain applications
  • Compatibility and migration
• Oracles and operator representations
  • Normalization constants
  • Data words and amplitudes
  • Open oracles
• QRAM data structures: pointers, offsets, and random writes
  • Pointers and reads
  • Multidimensional views
  • Random writes
  • Capability overview
• Quantum data structures and the KP recommendation system
  • QVector: the qsample vector
  • QMatrix: the sample-and-query matrix
  • The KP quantum recommendation system
  • The QMem direct path of QFVM
• Conventions owned by algorithms: starting from one gate
  • 1. Protocols and algorithm generators
  • 2. One gate is simultaneously a unitary, a state preparation, and an LCU input
  • 3. QLSS accepts this gate directly as b
  • 4. Where A’s parameters are read from
  • 5. Providing new inputs without inheriting from oracq
  • 6. What Hermitian, Trotterizable, and QSP each mean
  • 7. LCHS and Schrödingerization each own their conventions
  • 8. requires, reports, and binding each do one thing
  • 9. Running and engineering boundaries
• Implementing QPDE/QODE: multiple input paradigms, replaceable oracles, and protocols
  • 1. Division of labor from PDE to circuit
  • 2. Three kinds of objects, three replacement slots
  • 3. Which input paradigms a given oracle should allow
  • 4. LCHS: from inputs to the full generating function
  • 5. Schrödingerization: the auxiliary coordinate also stays in registers
  • 6. Carleman: writing nonlinearity as multilinear input ports
  • 7. How one linear QPDE swaps solvers
  • 8. Batched binding: when to swap an oracle and when to regenerate
  • 9. Output semantics, parameters, and validation responsibilities
  • 10. Running and validation
  • Numerical validation
• Automatically generating reversible quantum modules from ordinary math functions
  • The implemented mathematical scope
  • Approximation and status
  • Intermediate representations and backends
  • Command line and cases
  • Numerical validation
• QFVM input models and solver replacement
  • Data and quantum access
  • Constructing the problem
  • Replacing the QLSS
  • Output and norms
  • Line-by-line implementation walkthrough (including the QRAM data structures)
  • Numerical validation
  • QMem direct parallel path
• General QHAM automatic generation: PDE → HAM → QCL → QODE
  • 1. The precise scope of “generality”
  • 2. What the automatic derivation actually generates
  • 3. Two serializable representation layers
  • 4. How lowering to register-level quantum modules works
  • 5. The initial state cannot be normalized block by block
  • 6. The input model entering QODE
  • 7. Keeping implementations open
  • 8. The automatic-derivation utility
  • 9. Validation and implementation boundaries
  • 10. Numerical validation
  • 11. QRAM data path, line by line
• QCNN line-by-line implementation walkthrough
  • 1. End-to-end minimal example
  • 2. The classical basis line by line (qcnn.py)
  • 3. The quantum building blocks line by line (qcnn_layer.py)
  • 4. Validation matrix (all passing)
• Export and execution backends
  • OriginIR-ext
  • PySparQ
  • Reference executor
  • Readout
• Resource estimation (Toffoli+Clifford+T+QRAM)
  • Overview
  • Interface
  • Applicability boundaries and known gaps
  • Numerical validation
• Command line
  • Binding implementations
  • Open and closed resource analysis
  • Execution
  • Math functions and QHAM
• Import path migration
  • Split-up legacy entry points
  • File formats
  • Algorithm interface naming and the resource ledger
• Applicability boundaries and validation status
  • Scale limits
  • Mathematical claims
  • Results and readout