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Adjoint

As explained in Quantum Programming in Depth

The adjoint of a complex matrix or linear operator is its conjugate transpose, and for a unitary operator it equals the inverse.

The adjoint of a complex matrix or linear operator is its conjugate transpose. For a unitary operator, the adjoint is also the inverse, so applying it reverses the operator's action. section 4.3.1

In quantum measurement, an operation can map a noncomputational basis to computational-basis states before measurement. Apply its adjoint afterward when the post-measurement state must match the observed basis state; otherwise, the reversal can be omitted. section 4.3.1

Use these rules when reversing quantum computations:

  • Reverse composed operations in the opposite order, beginning with the operation performed last. section 8.2.2
  • Reuse the original computation for uncomputation when it implements a classical reversible function F(x, y) = (x, y ⊕ f(x)), because F applied twice returns the input. section 8.2.2
  • Uncompute independent constraint checks in any order; do not use that shortcut when later checks depend on earlier results. section 8.2.2

Bell-state measurement

Hadamard followed by CNOT maps computational-basis states to Bell states. To identify Bell states through computational-basis measurement, apply the adjoint: CNOT first, then Hadamard. section 4.3.1

Figure 4.5 Performing a measurement in a noncomputational basis, using the Bell measurement as an example. First, we apply an operation that maps the basis states of the measurement basis (in this case, the Bell states) to the computational basis states. Then, we measure the qubits in the computa-tional basis and interpret the measurement results. Finally, if the state of the system after the measure-ment has to match the measurement outcome, we apply the adjoint of the operation we applied in the first step.
figure

Trace the three-stage noncomputational-basis measurement workflow: transform, measure computationally, and optionally apply the adjoint. section 4.3.1

How Quantum Programming in Depth covers it

How deep it goes
Explains inverse action, gate-order reversal, and self-inverse classical reversible functions, without deriving matrix entries.
Examples
Uses N queens constraint checks, Hadamard-basis measurement, and Bell measurement to demonstrate adjoint application.
What it assumes
Assumes quantum states, unitary transformations, computational-basis measurement, and reversible functions.
What you can do after
Apply adjoints to reverse quantum operations and determine when repeated reversible code safely performs uncomputation.
How it connects
Connects unitary transformations, reversible computation, uncomputation, quantum measurement, and Bell states.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Quantum Programming in Depth. The section links point to the parts of the book it draws on.